Practically all things, both natural and man-made, in any aggregative state, are multicomponent mixtures. Even superpure chemically individual substances that have been subjected to multiple purification steps always contain a set of trace impurities. For a number of reasons, it is nearly impossible, using current techniques, to separate these impurities from the desired component.
For example, even ideally purified deionized water contains trace amounts, at least, of dozens of humus impurities (Lavrik et al., 2000). Distilled spirits produced by the highest separation efficiency contain more than 200 trace components (Karagodin, 1998). Marketable cyclohexane contains up to 70 trace components (Dedkov, 2002). Yet these three entities are considered to be pure substances.
The situation with industrial products is much more complicated, because in making these products a certain set of impurity components are introduced as part of the process of production, and these impurities determine the quality and consumption features of a product. Other components, which are sometimes present in trace amounts, determine the safety of the product.
At present, about 80,000 kinds of chemical products are manufactured all over the world, most of which products are toxic to warm-blooded animals (Korte, 1996). The appearance of these substances in the environment and, particularly, in life support systems, poses a serious threat. The problem of water quality on the planet is particularly important, because 90% of the water is consumed for industrial use and is returned to the environment with wastewater (Dedkov, 2002). In fact, more than 500 individual man-made substances have been detected in wastewater, almost half of which are regarded as very dangerous, while the rest are characterized as dangerously harmful to vital functions and the functioning of ecosystems (Fortoutan-Red, 1982).
In other words, in practically all fields of human activities, there is a need for effective analytical techniques so that dangerous substances can be detected and eliminated.
Traditional methods for analyzing compounds in multicomponent mixtures are usually based on selection of the analyte from the mixture, with further qualitative-quantitative correlation. This procedure requires a number of instrumental physical and chemical methods, often resulting in the requirement for preliminary treatment or concentration of a sample. Modern standardized analytical processes cover only about 20% of the total available set of components (Devjatykh et al., 1994). This results from the fact that the substances being analyzed may consist of tens and hundreds of individual compounds, and exhaustive analysis of the all of the components of a multicomponent mixture becomes a very long, laborious, and expensive procedure. At the same time, modern conditions dictate the requirement for rapid analysis methods which are simple in execution and which are able to rapidly detect the widest array of contaminants.
The methodology of analytical screening (Beyermann, 1982) relates to such methods, which itself is not strictly an analytical procedure. The essence of this methodology consists in rapid screening of suspicious objects requiring further standard by stricter methods. The parameters of these objects deviate from the parameters of standard object by any characteristic which is subject to rapid methods of instrumental control. It is evident that, for multicomponent mixtures, this characteristic is the identity or distinction of a compound in the product being analyzed, as compared with a standard compound which corresponds strictly with the manufacturer's process, or with a natural set of natural components wherein absence of the contaminants is confirmed. Thus, the solution of the task of rapid analysis does not require decoding of a qualitative-quantitative compound of a mixture. For that, it is quite enough to find an analytic method making it possible to receive some integral characteristic, or a set of characteristics, which can be measured instrumentally, ensuring unique identification of a compound in a mixture, and, at the same time, making it possible to detect qualitative changes of the compound at the level of trace amounts of a substance.
Optical spectrum methods of analysis are the most attractive for analyzing multicomponent mixtures. One peculiarity of optical spectra is the fact that they are characterized by complex reflection of both the component compound of the mixture and characteristics of individual components related to their chemical structure, and a complicated set of interactions of the components of the mixture with each other of a non-covalent nature (supra-molecular interactions [Lenn, 1995]. These interactions determine the per-molecular structure of a substance of nano-dimensional scale that is responsible for producing the optical spectra (Bakhshiev, 1972; Lakowicz, 1983; Suppan, 1990). In view of these circumstances, the optical spectra of multicomponent mixtures are uniquely sensitive to the slightest changes of a component in the mixture, which spectra appear both owing to mechanisms of intramolecular photonics and as a result of the influence of supramolecular interactions upon spectroscopic visualization of these mechanisms.
Photoabsorption methods are the most widely used analytical techniques. These methods are based on relative measurement of the light falling on a sample and passed through the sample. When these measurements are performed, systematic hardware inaccuracies are taken into account automatically, making these methods suitable for quantitative assessments. So, in particular, methods are known to analyze liquids for determining their identity or differences. Likewise, methods are known for determining contaminants in liquids, which methods consist of measuring absorption or reflectance spectra of an unknown liquid to determine characteristic spectra, finger prints, and/or profiles of data, and to determine the ratio of intensities of the light reflected or absorbed by a standard sample and a tested sample within the selected spectral section (Ingrum, 1991; Littlejohn, 1991). However, the absorption and reflectance spectra have low sensitivity and are ineffective for identifying trace amounts of impurities.
From Prior Art it is known that the luminescence spectra (Beyermann, 1982) are much more responsive to trace amounts of impurities and are very selective. At the same time, for identifying components of a mixture, three dimensional luminescence spectra (Webor, 1961) are most appropriate, which spectra reflect the complete set of spectral-luminescent characteristics of the test object. These spectra may be represented both as an isometric projection in coordinates of excitation wavelength and irradiation wavelength, or contoured spectra. Particularly, there is a known method for identifying a petroleum type by contoured luminescence excitation-irradiation spectra (Rho et al., 1978; Corfield et al., 1981). Also, very striking differences are observed for petroleum synthesis products. As an example for FIGS. 1 and 2 such data are given that were obtained by the present inventions. An illustration of this is found in FIG. 1 in which the contoured spectra for various brands of gasoline are shown.
The drawbacks of the luminescence spectra are that the excitation-irradiation spectra are not able to display a complete component makeup of a sample, but only part of the components. To receive contoured spectra, mathematical interpolation software is used, which inevitably distorts the analysis result, making them of little use for correct identification and/or determination of differences for mixtures which are similar to each other. More, as stated in Siegel et al., 1985, visual analysis by three-dimensional spectra for mixtures containing more than three components presents a quite insoluble problem. For example, FIG. 2 shows the contoured spectra for luminescence of tap water and the same water with added chlorine. As one can see, the visual differences of contoured spectra for these samples are imperceptible.
The closest solution has been the method of identification of spirit-based liquids by the difference in matrices composed using a complex set of spectral profiles of a light transmission ratio, and luminescence profiles normalized for a unit, when excited by different wavelengths of light (Nekrasov, Russian Patent RU 2150699, 2000). This method totally excludes the human factor, resulting in positive identity and differences in trace amounts of impurities, even for objects which are very close in their composition (Nekrasov et al., Theses, 2000). The drawbacks of this method are that analytical matrices used for identification contain data of absolute measurements of the intensities being analyzed, and do not take into account the spectral light transfer ratio of the optical path of an analytic device, and the spectral responsiveness of photodetectors. This makes it impossible to correctly compare the analysis results and practically excludes their use in forming centralized electronic data banks. Also, during the actual measurement, noise caused by intensity fluctuations of the light source, being particularly perceptible in the ultraviolet range of the spectrum, is not taken into account, thus influencing the selectivity of the method and its possibilities for identification.
At the same time, the spectral characteristics used in the methods described above (Corfield et al., 1981; Siegel et al., 1985; Nekrasov, Russian Patent RU 2150699, 2000) reflect, mainly, only those components of a mixture that have pronounced chromophore characteristics and/or luminescence. In both absorption and luminescence of multicomponent systems, the components not having their own chromophore and luminophore characteristics are shown weakly. Both methods, by objective reasons have an analytical range which is restricted by the high concentrations of components of the mixture, resulting in drastic restriction of the possibilities of identification where there are high concentrations of characteristic components. These circumstances do not allow one, particularly for ultraclean substances and compounds, to use referencing of the objects into classes and to effectively use online features of communications networks and electronic data banks for identification. It makes it impossible to identify unknown objects in the physical absence of a standard sample of a compound.